In the load_data chunk we need to load all the data we will need for the analysis. We have 2 data sources:
design: maps sample_ids to the conditions of the experimentmetabolomics: contains the measured intensities of each feature and mapping of metabolomics samples to sample_id.The data is stored respectively in 3 data.frames: df.design, df.metabolomics.raw and df.metabolomics.design.
NOTE: An important difference compared to the tutorial file is that df.metabolomics.design is already joined with df.design.
First thing to do when encountering a new dataset is to look at it’s shape and properties.
## Observations: 28,950
## Variables: 11
## $ bucket <chr> "344.07941 Da 18.36 s", "344.07941 Da 18.36 s", "344.…
## $ RT <dbl> 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.63, 0.63, 0.63,…
## $ mz_ratio <dbl> 345.0867, 345.0867, 345.0867, 345.0867, 345.0867, 345…
## $ Name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Formula <chr> "C20H12N2O4", "C20H12N2O4", "C20H12N2O4", "C20H12N2O4…
## $ phase <chr> "apolar", "apolar", "apolar", "apolar", "apolar", "ap…
## $ mass <dbl> 344.0794, 344.0794, 344.0794, 344.0794, 344.0794, 344…
## $ charge <dbl> 0.9970811, 0.9970811, 0.9970811, 0.9970811, 0.9970811…
## $ metabolomics_id <chr> "202002_TTP_SILS2665_RA_1_1 _merged_", "202002_TTP_SI…
## $ value <dbl> 10066, 9210, 9564, 8882, 9346, 8106, 3996, 4116, 4132…
## $ value_log10 <dbl> 4.002900, 3.964307, 3.980685, 3.948560, 3.970672, 3.9…
What is in data.frame and what do you think is the meaning of each column? Is the data in a tidy format?
As you might have notice, some of the features have a formula and/or a label associated to it but other don’t. In addition, there are features that have the same formula and/or label. Create a figure showing the percentage of features with a formula and with a name for each phase and a table showing the total number of features and with names and formulas and how many of those are unique for each phase.
| phase | total_count | n_formula | n_uniq_formula | n_label | n_uniq_label |
|---|---|---|---|---|---|
| apolar | 11742 | 8460 | 1237 | 108 | 16 |
| polar | 17208 | 8718 | 1240 | 660 | 89 |
df.metabolomics <- df.metabolomics.raw %>%
# remove metabolites with only 0 values
group_by(phase, bucket) %>%
filter(sum(value) > 0) %>%
mutate(
# replace zero values by half of the smallest observed value for that metabolite
value_cor = ifelse(value <= 0, min(value[value > 0], na.rm = T) / 2, value),
# calculate reference per metabolite
pseudo_reference = ifelse(any(value_cor == 0), NA, mean(log(value_cor)))
) %>%
group_by(phase, metabolomics_id) %>%
mutate(
# calculate median size factor per sample
size_factor = exp(median(log(value_cor) - pseudo_reference, na.rm=T)),
# correct value with size factor
value_norm = value_cor / size_factor,
value_norm_log10 = log10(value_norm)
) %>%
# in this report I assume the data.frame to be grouped by phase only, so I set this grouping explicitly
group_by(phase)
df.pca <- df.metabolomics %>%
mutate(
value_log_norm = log10(value_norm)
) %>%
select(phase, bucket, metabolomics_id, value_log_norm) %>%
filter(!is.na(bucket)) %>%
group_by(phase) %>%
f_pca(features_from = "bucket", counts_from = "value_log_norm", samples_from = "metabolomics_id")
To extract the top- and bottom- 10 loadings we leverage the power of our nested data.frame and map:
df.pca.loadings <- df.pca %>%
mutate(
top_10_pc_1 = map(pca_loadings, function(.d) {
.d %>% filter(PC == 1) %>% arrange(-loading) %>% slice(1:10)
}),
bottom_10_pc_1 = map(pca_loadings, function(.d) {
.d %>% filter(PC == 1) %>% arrange(loading) %>% slice(1:10)
})
)
Most down-regulated metabolites of PC 1:
| phase | bucket | loading |
|---|---|---|
| apolar | 870.56984 Da 803.03 s | -0.1827206 |
| apolar | 892.54105 Da 803.03 s | -0.1272318 |
| apolar | 551.42610 Da 842.78 s | -0.1212339 |
| apolar | 886.56398 Da 850.98 s | -0.0992992 |
| apolar | 396.00441 Da 41.68 s | -0.0990238 |
| apolar | 792.56643 Da 505.34 s | -0.0989117 |
| apolar | 349.96167 Da 41.67 s | -0.0960333 |
| apolar | 1052.52432 Da 807.07 s | -0.0838531 |
| apolar | 381.98812 Da 41.95 s | -0.0800602 |
| apolar | 335.94565 Da 41.39 s | -0.0766024 |
| polar | 202.16809 Da 35.67 s | -0.0919538 |
| polar | 545.16130 Da 170.88 s | -0.0792119 |
| polar | 551.17860 Da 52.61 s | -0.0760986 |
| polar | 1389.01032 Da 439.32 s | -0.0755749 |
| polar | 279.13232 Da 47.75 s | -0.0752341 |
| polar | 186.01639 Da 214.63 s | -0.0749046 |
| polar | 505.17065 Da 53.87 s | -0.0695598 |
| polar | 1275.92760 Da 432.91 s | -0.0695352 |
| polar | 143.09457 Da 35.95 s | -0.0663302 |
| polar | 153.98977 Da 215.00 s | -0.0651861 |
Most up-regulated metabolites of PC 1:
| phase | bucket | loading |
|---|---|---|
| apolar | 195.05665 Da 40.87 s | 0.2397694 |
| apolar | 412.09570 Da 40.31 s | 0.1526381 |
| apolar | 175.96327 Da 18.43 s | 0.1121804 |
| apolar | 467.00597 Da 39.82 s | 0.1100497 |
| apolar | 131.97352 Da 18.49 s | 0.0958542 |
| apolar | 243.95081 Da 24.01 s | 0.0843453 |
| apolar | 379.92616 Da 20.53 s | 0.0822617 |
| apolar | 311.93825 Da 20.61 s | 0.0814280 |
| apolar | 243.95122 Da 70.97 s | 0.0779820 |
| apolar | 379.92574 Da 33.46 s | 0.0734391 |
| polar | 195.05672 Da 46.20 s | 0.1721499 |
| polar | 780.22632 Da 47.97 s | 0.1497580 |
| polar | 684.08708 Da 50.27 s | 0.1309272 |
| polar | 330.11082 Da 47.20 s | 0.1175668 |
| polar | 443.02423 Da 50.64 s | 0.0849878 |
| polar | 537.17417 Da 52.41 s | 0.0833498 |
| polar | 218.04115 Da 45.53 s | 0.0828316 |
| polar | 343.95574 Da 45.53 s | 0.0788116 |
| polar | 342.11236 Da 47.50 s | 0.0745676 |
| polar | 507.04174 Da 50.76 s | 0.0725621 |
Another useful tool is to plot the data in a heatmap.
This approach takes
df.heatmap <- right_join(
# take our normalised values (these will be shown in the heatmap)
df.metabolomics %>% select(phase, bucket, metabolomics_id, value_norm),
# extract the top- and bottom-10 from the nested data.frame
# if a bucket is in top- or bottom-10 it will be retained in our joined data frame
bind_rows(
df.pca.loadings %>% unnest(cols = top_10_pc_1) %>% select(phase, bucket, loading),
df.pca.loadings %>% unnest(cols = bottom_10_pc_1) %>% select(phase, bucket, loading)
),
by = c("phase", "bucket")
) %>%
# add design information
left_join(
df.metabolomics.design %>% select(sample_id, metabolomics_id, purpose, phosphate_mM),
by= c("metabolomics_id")
) %>%
select(phase, bucket, sample_id, value_norm) %>%
pivot_wider(
names_from = sample_id,
values_from = value_norm
) %>%
ungroup()
We can generalize our volcano plot code into a function to avoid repeating the same code over and over…
f_plot_volcano <- function(.d, .title = "Volcano Plot") {
.d %>%
ggplot(aes(x = logFC, y = -log10(padj), colour = threshold)) +
facet_wrap(~phase) +
geom_point(alpha = 0.5) +
geom_vline(xintercept = 0, colour = "black") +
geom_vline(xintercept = c(-fold.change.threshold, fold.change.threshold), colour = "red") +
geom_hline(yintercept = -log10(alpha), colour = "red") +
geom_text_repel(
aes(x = logFC, y = -log10(padj), label = bucket),
# feature of ggplot; it applies the function to the overall dataset -> only label those which breach our thresholds
data = function(.d) { .d %>% filter(threshold) },
colour = "grey20"
) +
theme(legend.position = "none") +
labs(
title = .title,
x = "Effect size: log2(fold-change)",
y = "-log10(adjusted p-value)"
)
}
Finally print a table with all interesting buckets.
Polar Phase
| phase | bucket | Name | Formula | logFC | p.value | padj |
|---|---|---|---|---|---|---|
| polar | 1389.01032 Da 439.32 s | NA | C55H112N44 | -4.404664 | 0.0000000 | 0.0000000 |
| polar | 1275.92760 Da 432.91 s | NA | C70H134NO14PS | -4.063928 | 0.0000000 | 0.0000000 |
| polar | 505.17065 Da 53.87 s | NA | C13H36N3O13PS | -4.115039 | 0.0000000 | 0.0000000 |
| polar | 113.08366 Da 47.02 s | NA | NA | 3.450712 | 0.0000000 | 0.0000000 |
| polar | 248.97570 Da 51.41 s | NA | NA | 3.866483 | 0.0000000 | 0.0000000 |
| polar | 162.05319 Da 53.18 s | NA | C6H10O5 | -2.891696 | 0.0000000 | 0.0000000 |
| polar | 99.06825 Da 48.34 s | NA | C5H9NO | 3.982592 | 0.0000000 | 0.0000000 |
| polar | 324.10542 Da 52.62 s | NA | C17H16N4OS | -2.856964 | 0.0000000 | 0.0000000 |
| polar | 557.08069 Da 53.14 s | NA | NA | -2.824540 | 0.0000000 | 0.0000000 |
| polar | 135.05421 Da 46.92 s | NA | NA | -3.754185 | 0.0000000 | 0.0000000 |
| polar | 653.80118 Da 35.63 s | NA | C15H6N6O8S8 | -2.730258 | 0.0000000 | 0.0000000 |
| polar | 162.05294 Da 48.25 s | NA | C6H10O5 | -3.936065 | 0.0000000 | 0.0000000 |
| polar | 307.01122 Da 49.47 s | NA | C8H9N3O8S | -2.768852 | 0.0000000 | 0.0000000 |
| polar | 118.02789 Da 46.85 s | NA | C5H2N4 | -2.308795 | 0.0000000 | 0.0000000 |
| polar | 218.04115 Da 45.53 s | NA | C5H10N6S2 | 4.687774 | 0.0000000 | 0.0000000 |
| polar | 195.05672 Da 46.20 s | 2-_N-morpholino_ethanesulfonic acid | C6H13NO4S | 9.770304 | 0.0000000 | 0.0000000 |
| polar | 281.91637 Da 34.82 s | NA | C8H2N4S4 | -2.635089 | 0.0000000 | 0.0000000 |
| polar | 684.08708 Da 50.27 s | NA | C19H28N10O10S4 | 7.320491 | 0.0000000 | 0.0000000 |
| polar | 851.81735 Da 35.69 s | NA | NA | 2.225573 | 0.0000000 | 0.0000000 |
| polar | 443.02423 Da 50.64 s | NA | C21H10N5O3PS | 4.805384 | 0.0000000 | 0.0000000 |
| polar | 54.04650 Da 51.02 s | NA | NA | -2.001179 | 0.0000000 | 0.0000000 |
| polar | 80.96480 Da 48.87 s | NA | NA | 3.110823 | 0.0000000 | 0.0000000 |
| polar | 551.17860 Da 52.61 s | NA | C24H42NO3PS4 | -4.414098 | 0.0000000 | 0.0000000 |
| polar | 182.20346 Da 1169.50 s | NA | C13H26 | -2.063661 | 0.0000000 | 0.0000000 |
| polar | 330.11082 Da 47.20 s | NA | C19H14N4O2 | 6.650154 | 0.0000000 | 0.0000000 |
| polar | 666.22332 Da 65.72 s | NA | C17H44N6O17P2 | -2.052050 | 0.0000000 | 0.0000000 |
| polar | 153.98977 Da 215.00 s | NA | C6H2O5 | -3.738726 | 0.0000000 | 0.0000000 |
| polar | 135.05435 Da 47.06 s | Adenine | C5H5N5 | -2.358729 | 0.0000000 | 0.0000000 |
| polar | 468.37287 Da 1179.28 s | NA | C28H53O3P | -3.145643 | 0.0000000 | 0.0000000 |
| polar | 505.42821 Da 1174.94 s | NA | C35H55NO | -2.193067 | 0.0000000 | 0.0000000 |
| polar | 254.09980 Da 49.53 s | NA | C9H18O8 | -3.317741 | 0.0000000 | 0.0000000 |
| polar | 491.44838 Da 1179.75 s | NA | C27H61N3O2S | -2.666261 | 0.0000000 | 0.0000000 |
| polar | 202.16809 Da 35.67 s | NA | C10H22N2O2 | -2.702719 | 0.0000000 | 0.0000000 |
| polar | 64.00597 Da 46.78 s | NA | NA | -2.367665 | 0.0000000 | 0.0000000 |
| polar | 115.90590 Da 28.82 s | NA | NA | 2.108976 | 0.0000000 | 0.0000000 |
| polar | 585.17066 Da 47.79 s | NA | C19H35N7O8S3 | 2.019161 | 0.0000000 | 0.0000000 |
| polar | 279.13232 Da 47.75 s | NA | C12H25NO2S2 | -4.373231 | 0.0000000 | 0.0000000 |
| polar | 143.09457 Da 35.95 s | NA | C7H13NO2 | -2.340761 | 0.0000000 | 0.0000000 |
| polar | 390.11373 Da 47.79 s | NA | C12H18N6O9 | 2.049031 | 0.0000000 | 0.0000000 |
| polar | 302.06080 Da 66.17 s | NA | C8H18N2O6S2 | 2.167386 | 0.0000000 | 0.0000000 |
| polar | 211.05125 Da 67.62 s | NA | C6H13NO5S | 2.305008 | 0.0000000 | 0.0000001 |
| polar | 233.03331 Da 68.81 s | NA | C12H11NS2 | 2.310754 | 0.0000000 | 0.0000001 |
| polar | 780.22632 Da 47.97 s | NA | C27H53N6O8PS5 | 9.068171 | 0.0000000 | 0.0000001 |
| polar | 324.10409 Da 67.10 s | NA | C17H24S3 | -2.232661 | 0.0000000 | 0.0000001 |
| polar | 309.96791 Da 67.81 s | NA | C8H3N6O4PS | 2.340419 | 0.0000000 | 0.0000001 |
| polar | 418.57380 Da 48.36 s | NA | NA | 2.475847 | 0.0000000 | 0.0000002 |
| polar | 255.96926 Da 300.67 s | NA | NA | 2.381188 | 0.0000000 | 0.0000002 |
| polar | 440.14031 Da 52.94 s | NA | C15H20N8O8 | 2.408047 | 0.0000000 | 0.0000003 |
| polar | 636.12403 Da 101.36 s | NA | C17H44N6O3S8 | -2.155888 | 0.0000000 | 0.0000003 |
| polar | 186.01639 Da 214.63 s | NA | C7H6O6 | -4.430408 | 0.0000000 | 0.0000004 |
| polar | 312.13616 Da 50.15 s | NA | C19H20O4 | 2.482399 | 0.0000001 | 0.0000007 |
| polar | 975.28149 Da 48.06 s | NA | C33H49N23OS6 | 2.621164 | 0.0000001 | 0.0000010 |
| polar | 657.89435 Da 36.06 s | NA | C20H7N10O5PS5 | 2.118107 | 0.0000001 | 0.0000010 |
| polar | 86.03658 Da 36.94 s | NA | NA | -2.371961 | 0.0000001 | 0.0000010 |
| polar | 85.05253 Da 37.75 s | NA | C4H7NO | -2.292786 | 0.0000001 | 0.0000010 |
| polar | 211.05153 Da 67.98 s | NA | C6H13NO5S | 2.909145 | 0.0000003 | 0.0000023 |
| polar | 195.45403 Da 48.06 s | NA | NA | 2.907084 | 0.0000004 | 0.0000028 |
| polar | 310.12002 Da 45.46 s | NA | C11H22N2O6S | 2.742540 | 0.0000006 | 0.0000039 |
| polar | 195.05656 Da 124.38 s | NA | C6H13NO4S | 2.920660 | 0.0000006 | 0.0000041 |
| polar | 424.11927 Da 45.00 s | NA | C13H32N2O5S4 | 3.111636 | 0.0000007 | 0.0000043 |
| polar | 342.11236 Da 47.50 s | NA | C19H18O6 | 5.026800 | 0.0000007 | 0.0000046 |
| polar | 313.56890 Da 46.09 s | NA | NA | 3.155939 | 0.0000009 | 0.0000057 |
| polar | 753.53621 Da 865.22 s | NA | NA | -2.903183 | 0.0000009 | 0.0000059 |
| polar | 72.02134 Da 43.52 s | NA | C3H4O2 | 2.415400 | 0.0000011 | 0.0000067 |
| polar | 390.67583 Da 47.24 s | NA | NA | 3.298686 | 0.0000012 | 0.0000072 |
| polar | 732.22414 Da 45.42 s | NA | C19H41N16O7PS3 | 3.223633 | 0.0000015 | 0.0000092 |
| polar | 107.98852 Da 67.88 s | NA | NA | 3.461934 | 0.0000016 | 0.0000097 |
| polar | 197.03605 Da 160.38 s | NA | NA | 3.401431 | 0.0000018 | 0.0000107 |
| polar | 537.17417 Da 52.41 s | NA | C13H40N5O9PS3 | 5.521678 | 0.0000018 | 0.0000109 |
| polar | 684.08553 Da 49.48 s | NA | C25H9N20O4P | 3.583969 | 0.0000019 | 0.0000113 |
| polar | 192.02681 Da 83.77 s | Isocitric acid | C6H8O7 | -2.705202 | 0.0000024 | 0.0000139 |
| polar | 626.16820 Da 185.51 s | NA | C19H47O12PS4 | 3.265538 | 0.0000025 | 0.0000142 |
| polar | 569.14799 Da 188.92 s | NA | C27H27N3O9S | 3.538897 | 0.0000041 | 0.0000222 |
| polar | 293.11182 Da 74.41 s | NA | C12H23NO3S2 | -2.212128 | 0.0000050 | 0.0000262 |
| polar | 474.01089 Da 47.04 s | NA | NA | -2.037417 | 0.0000053 | 0.0000279 |
| polar | 424.14514 Da 52.69 s | NA | C16H32N4OS4 | 3.735806 | 0.0000064 | 0.0000330 |
| polar | 424.14674 Da 52.13 s | NA | C13H33N2O7PS2 | 4.570111 | 0.0000070 | 0.0000356 |
| polar | 507.04174 Da 50.76 s | NA | C21H5N11O6 | 4.651619 | 0.0000071 | 0.0000358 |
| polar | 549.85850 Da 29.05 s | NA | NA | 2.026331 | 0.0000080 | 0.0000401 |
| polar | 135.98121 Da 29.42 s | NA | NA | 2.270894 | 0.0000086 | 0.0000428 |
| polar | 153.12665 Da 47.66 s | NA | C8H15N3 | -2.111851 | 0.0000128 | 0.0000614 |
| polar | 521.19632 Da 52.29 s | NA | C32H23N7O | -2.246412 | 0.0000130 | 0.0000618 |
| polar | 335.08973 Da 101.06 s | NA | C9H23NO8P2 | -2.014927 | 0.0000185 | 0.0000855 |
| polar | 535.03645 Da 50.76 s | NA | C16H6N15O6P | 3.907807 | 0.0000206 | 0.0000924 |
| polar | 225.03063 Da 215.72 s | NA | C6H11NO6S | 2.178694 | 0.0000227 | 0.0000990 |
| polar | 187.19382 Da 422.38 s | NA | C11H25NO | 3.732177 | 0.0000287 | 0.0001207 |
| polar | 330.11118 Da 45.92 s | NA | C19H22OS2 | 3.572680 | 0.0000300 | 0.0001246 |
| polar | 195.07332 Da 46.94 s | NA | NA | 4.865156 | 0.0000347 | 0.0001397 |
| polar | 553.82966 Da 26.74 s | NA | NA | 3.671634 | 0.0000361 | 0.0001438 |
| polar | 128.04615 Da 784.34 s | NA | NA | -2.372932 | 0.0000529 | 0.0001990 |
| polar | 256.94792 Da 41.13 s | NA | C5H7NO5S3 | 2.107670 | 0.0000536 | 0.0002017 |
| polar | 429.08512 Da 235.88 s | NA | C24H15NO7 | -3.966080 | 0.0000562 | 0.0002090 |
| polar | 228.10056 Da 337.15 s | NA | C12H20S2 | -2.111015 | 0.0000576 | 0.0002128 |
| polar | 689.03853 Da 100.75 s | NA | NA | -2.260020 | 0.0000633 | 0.0002308 |
| polar | 271.09782 Da 40.14 s | NA | C12H18NO4P | -2.046353 | 0.0000674 | 0.0002434 |
| polar | 356.12601 Da 54.83 s | NA | C20H20O6 | 5.723925 | 0.0000786 | 0.0002778 |
| polar | 343.95574 Da 45.53 s | NA | NA | 4.570205 | 0.0000856 | 0.0002987 |
| polar | 315.10435 Da 40.18 s | NA | C16H17N3O2S | 3.445711 | 0.0001049 | 0.0003585 |
| polar | 565.83665 Da 29.00 s | NA | C13H3N4O12PS4 | 2.332420 | 0.0001126 | 0.0003827 |
| polar | 406.27013 Da 988.50 s | NA | C13H34N12OS | -2.724868 | 0.0001250 | 0.0004199 |
| polar | 353.01730 Da 49.08 s | NA | C10H15N3O5S3 | -2.600020 | 0.0001300 | 0.0004334 |
| polar | 390.95388 Da 43.93 s | NA | C10H9N5O4S4 | 2.018588 | 0.0001491 | 0.0004882 |
| polar | 197.11625 Da 49.80 s | NA | C9H15N3O2 | -2.151283 | 0.0001495 | 0.0004890 |
| polar | 107.98851 Da 151.00 s | NA | NA | 3.289306 | 0.0001550 | 0.0005045 |
| polar | 582.17084 Da 54.22 s | NA | C36H27N2O4P | 2.037894 | 0.0001676 | 0.0005382 |
| polar | 81.98175 Da 1174.56 s | NA | NA | -3.701889 | 0.0001720 | 0.0005488 |
| polar | 407.90954 Da 42.95 s | NA | NA | 2.751582 | 0.0002041 | 0.0006399 |
| polar | 594.18444 Da 187.01 s | NA | C43H22N4 | -2.119586 | 0.0002277 | 0.0007060 |
| polar | 215.11621 Da 403.33 s | NA | C10H17NO4 | -2.767454 | 0.0003425 | 0.0010097 |
| polar | 576.13728 Da 560.21 s | NA | NA | -2.247904 | 0.0003452 | 0.0010165 |
| polar | 56.06225 Da 1178.52 s | NA | NA | -2.424346 | 0.0004098 | 0.0011752 |
| polar | 159.12589 Da 38.85 s | NA | NA | -2.345349 | 0.0004291 | 0.0012246 |
| polar | 351.08872 Da 183.84 s | NA | C7H13N9O8 | -3.479490 | 0.0005237 | 0.0014622 |
| polar | 270.14690 Da 581.44 s | NA | C14H22O5 | -2.152940 | 0.0008794 | 0.0023354 |
| polar | 327.56754 Da 50.04 s | NA | NA | 2.483253 | 0.0010959 | 0.0028548 |
| polar | 143.16765 Da 179.80 s | NA | C9H21N | 3.126743 | 0.0014405 | 0.0036369 |
| polar | 162.99232 Da 30.05 s | NA | NA | 3.712654 | 0.0017044 | 0.0041816 |
| polar | 130.99596 Da 29.67 s | NA | NA | 2.865890 | 0.0019884 | 0.0048043 |
| polar | 174.53774 Da 211.84 s | NA | NA | -2.387574 | 0.0026957 | 0.0063143 |
| polar | 311.55770 Da 52.79 s | NA | NA | 2.568152 | 0.0027307 | 0.0063777 |
| polar | 835.84772 Da 33.22 s | NA | C25H14O21P6 | 3.225463 | 0.0029057 | 0.0067014 |
| polar | 774.52839 Da 1183.19 s | NA | C32H75N10O7PS | 2.758175 | 0.0033151 | 0.0075219 |
| polar | 641.08784 Da 50.18 s | NA | C14H32N11O6PS5 | 3.388157 | 0.0034151 | 0.0077244 |
| polar | 312.26765 Da 1181.50 s | NA | NA | 2.938936 | 0.0042354 | 0.0092726 |
| polar | 611.17805 Da 1184.30 s | NA | C28H38NO8PS2 | 3.706555 | 0.0042868 | 0.0093637 |
Apolar Phase
| phase | bucket | Name | Formula | logFC | p.value | padj |
|---|---|---|---|---|---|---|
| apolar | 467.00597 Da 39.82 s | NA | C20H9N3O9S | 4.091001 | 0.0000000 | 0.0000000 |
| apolar | 1052.52432 Da 807.07 s | NA | C65H84N2S5 | -2.031740 | 0.0000000 | 0.0000000 |
| apolar | 792.56643 Da 505.34 s | NA | NA | -2.786192 | 0.0000000 | 0.0000000 |
| apolar | 870.56984 Da 803.03 s | NA | C51H84O7P2 | -6.288501 | 0.0000000 | 0.0000001 |
| apolar | 191.04414 Da 41.28 s | NA | NA | 2.316799 | 0.0000000 | 0.0000002 |
| apolar | 892.54105 Da 803.03 s | NA | C52H81N2O4PS2 | -4.097175 | 0.0000001 | 0.0000004 |
| apolar | 244.90652 Da 77.09 s | NA | NA | -2.088680 | 0.0000001 | 0.0000009 |
| apolar | 195.05665 Da 40.87 s | 2-_N-morpholino_ethanesulfonic acid | C6H13NO4S | 8.717947 | 0.0000003 | 0.0000020 |
| apolar | 412.09570 Da 40.31 s | NA | C11H24N8OS4 | 5.281069 | 0.0000017 | 0.0000080 |
| apolar | 349.96167 Da 41.67 s | NA | NA | -2.885525 | 0.0000252 | 0.0000832 |
| apolar | 886.56398 Da 850.98 s | NA | C34H66N26OS | -2.770792 | 0.0000922 | 0.0002589 |
| apolar | 396.00441 Da 41.68 s | NA | NA | -2.197764 | 0.0001141 | 0.0003107 |
| apolar | 381.98812 Da 41.95 s | NA | C18H7O8P | -2.699859 | 0.0001375 | 0.0003630 |
| apolar | 652.36412 Da 499.78 s | NA | C25H58N4O11P2 | 2.075934 | 0.0002266 | 0.0005636 |
| apolar | 551.42610 Da 842.78 s | NA | NA | -3.191314 | 0.0003743 | 0.0008795 |
| apolar | 335.94565 Da 41.39 s | NA | NA | -2.081601 | 0.0007243 | 0.0015716 |
## `mutate_if()` ignored the following grouping variables:
## Columns `phase`, `bucket`
T-test results were exported to ./data/processed/metabolomics/20200311_metabolomics_ttest.csv